Overview

Dataset statistics

Number of variables22
Number of observations9109
Missing cells2225
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory176.0 B

Variable types

DateTime2
Numeric12
Categorical6
Text2

Alerts

depth is highly overall correlated with dmin and 1 other fieldsHigh correlation
depthError is highly overall correlated with dminHigh correlation
dmin is highly overall correlated with depth and 3 other fieldsHigh correlation
gap is highly overall correlated with nstHigh correlation
latitude is highly overall correlated with locationSource and 3 other fieldsHigh correlation
locationSource is highly overall correlated with latitude and 4 other fieldsHigh correlation
longitude is highly overall correlated with latitude and 3 other fieldsHigh correlation
mag is highly overall correlated with dmin and 2 other fieldsHigh correlation
magNst is highly overall correlated with nstHigh correlation
magSource is highly overall correlated with latitude and 4 other fieldsHigh correlation
net is highly overall correlated with latitude and 4 other fieldsHigh correlation
nst is highly overall correlated with gap and 2 other fieldsHigh correlation
rms is highly overall correlated with depth and 2 other fieldsHigh correlation
status is highly overall correlated with locationSource and 2 other fieldsHigh correlation
magType is highly imbalanced (54.9%)Imbalance
type is highly imbalanced (92.3%)Imbalance
nst has 308 (3.4%) missing valuesMissing
gap has 310 (3.4%) missing valuesMissing
dmin has 310 (3.4%) missing valuesMissing
horizontalError has 553 (6.1%) missing valuesMissing
magError has 352 (3.9%) missing valuesMissing
magNst has 309 (3.4%) missing valuesMissing
id has unique valuesUnique
updated has unique valuesUnique
depth has 95 (1.0%) zerosZeros
dmin has 461 (5.1%) zerosZeros
horizontalError has 1429 (15.7%) zerosZeros

Reproduction

Analysis started2025-12-13 13:50:24.061276
Analysis finished2025-12-13 13:50:58.886985
Duration34.83 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

time
Date

Distinct9107
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size71.3 KiB
Minimum2025-11-11 02:55:00.360000+00:00
Maximum2025-12-11 02:44:15.295000+00:00
Invalid dates0
Invalid dates (%)0.0%
2025-12-13T19:20:59.072701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:59.369477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

latitude
Real number (ℝ)

High correlation 

Distinct7522
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.703301
Minimum-61.1342
Maximum82.1015
Zeros0
Zeros (%)0.0%
Negative300
Negative (%)3.3%
Memory size71.3 KiB
2025-12-13T19:20:59.611954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-61.1342
5-th percentile17.953033
Q133.691333
median38.815834
Q357.8199
95-th percentile62.201002
Maximum82.1015
Range143.2357
Interquartile range (IQR)24.128567

Descriptive statistics

Standard deviation17.625501
Coefficient of variation (CV)0.43302387
Kurtosis5.1094791
Mean40.703301
Median Absolute Deviation (MAD)6.720834
Skewness-1.5300459
Sum370766.37
Variance310.65829
MonotonicityNot monotonic
2025-12-13T19:20:59.864737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.8238334713
 
0.1%
38.8235015912
 
0.1%
38.8246650711
 
0.1%
38.8214988710
 
0.1%
38.8316650410
 
0.1%
38.8351669310
 
0.1%
38.8248329210
 
0.1%
38.8344993610
 
0.1%
38.8338317910
 
0.1%
38.835834510
 
0.1%
Other values (7512)9003
98.8%
ValueCountFrequency (%)
-61.13421
< 0.1%
-61.1321
< 0.1%
-60.26411
< 0.1%
-60.181
< 0.1%
-60.12881
< 0.1%
-60.08471
< 0.1%
-60.04841
< 0.1%
-58.98141
< 0.1%
-58.80891
< 0.1%
-58.20411
< 0.1%
ValueCountFrequency (%)
82.10151
< 0.1%
79.4481
< 0.1%
73.34251
< 0.1%
73.27771
< 0.1%
73.21631
< 0.1%
73.18761
< 0.1%
73.16921
< 0.1%
69.578277591
< 0.1%
68.612014771
< 0.1%
68.506492611
< 0.1%

longitude
Real number (ℝ)

High correlation 

Distinct7902
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-114.19558
Minimum-179.9638
Maximum179.9727
Zeros0
Zeros (%)0.0%
Negative8637
Negative (%)94.8%
Memory size71.3 KiB
2025-12-13T19:21:00.130431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-179.9638
5-th percentile-156.13962
Q1-148.9031
median-122.753
Q3-115.66317
95-th percentile23.07112
Maximum179.9727
Range359.9365
Interquartile range (IQR)33.239933

Descriptive statistics

Standard deviation61.128945
Coefficient of variation (CV)-0.53530045
Kurtosis11.537044
Mean-114.19558
Median Absolute Deviation (MAD)17.204602
Skewness3.350703
Sum-1040207.5
Variance3736.7479
MonotonicityNot monotonic
2025-12-13T19:21:00.362279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.818496711
 
0.1%
-122.81716929
 
0.1%
-122.81600198
 
0.1%
-122.81549848
 
0.1%
-122.81316387
 
0.1%
-122.81666567
 
0.1%
-122.8173377
 
0.1%
-122.81483467
 
0.1%
-122.81649787
 
0.1%
-122.79683697
 
0.1%
Other values (7892)9031
99.1%
ValueCountFrequency (%)
-179.96381
< 0.1%
-179.93111
< 0.1%
-179.86431
< 0.1%
-179.8631
< 0.1%
-179.85681
< 0.1%
-179.73821
< 0.1%
-179.27220151
< 0.1%
-179.21932981
< 0.1%
-179.1981
< 0.1%
-179.19251
< 0.1%
ValueCountFrequency (%)
179.97271
< 0.1%
179.92181
< 0.1%
179.90721
< 0.1%
179.85711
< 0.1%
179.79063421
< 0.1%
179.72326661
< 0.1%
179.6541
< 0.1%
179.63841
< 0.1%
179.59683331
< 0.1%
179.55951
< 0.1%

depth
Real number (ℝ)

High correlation  Zeros 

Distinct4685
Distinct (%)51.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.213377
Minimum-3.39
Maximum660.76
Zeros95
Zeros (%)1.0%
Negative298
Negative (%)3.3%
Memory size71.3 KiB
2025-12-13T19:21:00.893710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-3.39
5-th percentile0.24193362
Q12.6461372
median7.074
Q314.21
95-th percentile88.266984
Maximum660.76
Range664.15
Interquartile range (IQR)11.563863

Descriptive statistics

Standard deviation50.165355
Coefficient of variation (CV)2.4817899
Kurtosis74.060437
Mean20.213377
Median Absolute Deviation (MAD)4.814
Skewness7.5494978
Sum184123.65
Variance2516.5628
MonotonicityNot monotonic
2025-12-13T19:21:01.124722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10399
 
4.4%
5229
 
2.5%
095
 
1.0%
6.099981
 
0.9%
3554
 
0.6%
223
 
0.3%
2.02999997115
 
0.2%
8.911115
 
0.2%
1.95000004812
 
0.1%
1.711
 
0.1%
Other values (4675)8175
89.7%
ValueCountFrequency (%)
-3.391
 
< 0.1%
-3.281
 
< 0.1%
-3.261
 
< 0.1%
-3.191
 
< 0.1%
-3.181
 
< 0.1%
-3.171
 
< 0.1%
-3.161
 
< 0.1%
-3.151
 
< 0.1%
-3.031
 
< 0.1%
-2.983
< 0.1%
ValueCountFrequency (%)
660.761
< 0.1%
648.6481
< 0.1%
637.5091
< 0.1%
637.1381
< 0.1%
630.9851
< 0.1%
625.6131
< 0.1%
623.1091
< 0.1%
621.0631
< 0.1%
607.0931
< 0.1%
603.6591
< 0.1%

mag
Real number (ℝ)

High correlation 

Distinct1718
Distinct (%)18.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.6558258
Minimum-1.42
Maximum7.6
Zeros10
Zeros (%)0.1%
Negative428
Negative (%)4.7%
Memory size71.3 KiB
2025-12-13T19:21:01.401246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.42
5-th percentile0.03
Q10.82
median1.47
Q32.1356625
95-th percentile4.4830191
Maximum7.6
Range9.02
Interquartile range (IQR)1.3156625

Descriptive statistics

Standard deviation1.2486478
Coefficient of variation (CV)0.75409371
Kurtosis0.90846358
Mean1.6558258
Median Absolute Deviation (MAD)0.65563717
Skewness0.93354691
Sum15081.261
Variance1.5591214
MonotonicityNot monotonic
2025-12-13T19:21:01.639877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5171
 
1.9%
1.6146
 
1.6%
1.7116
 
1.3%
1.8112
 
1.2%
1.993
 
1.0%
4.590
 
1.0%
1.189
 
1.0%
4.488
 
1.0%
4.382
 
0.9%
1.481
 
0.9%
Other values (1708)8040
88.3%
ValueCountFrequency (%)
-1.421
 
< 0.1%
-1.311
 
< 0.1%
-1.291
 
< 0.1%
-1.241
 
< 0.1%
-1.231
 
< 0.1%
-1.182
< 0.1%
-1.173
< 0.1%
-1.163
< 0.1%
-1.132
< 0.1%
-1.121
 
< 0.1%
ValueCountFrequency (%)
7.61
 
< 0.1%
71
 
< 0.1%
6.62
 
< 0.1%
61
 
< 0.1%
5.91
 
< 0.1%
5.86
0.1%
5.75
0.1%
5.66
0.1%
5.59
0.1%
5.49
0.1%

magType
Categorical

Imbalance 

Distinct8
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size71.3 KiB
ml
5444 
md
2873 
mb
661 
mww
 
83
mwr
 
18
Other values (3)
 
29

Length

Max length5
Median length2
Mean length2.0130654
Min length2

Characters and Unicode

Total characters18335
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowml
2nd rowml
3rd rowml
4th rowml
5th rowmd

Common Values

ValueCountFrequency (%)
ml5444
59.8%
md2873
31.5%
mb661
 
7.3%
mww83
 
0.9%
mwr18
 
0.2%
mw15
 
0.2%
mh8
 
0.1%
mb_lg6
 
0.1%
(Missing)1
 
< 0.1%

Length

2025-12-13T19:21:01.842432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-13T19:21:01.999653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ml5444
59.8%
md2873
31.5%
mb661
 
7.3%
mww83
 
0.9%
mwr18
 
0.2%
mw15
 
0.2%
mh8
 
0.1%
mb_lg6
 
0.1%

Most occurring characters

ValueCountFrequency (%)
m9108
49.7%
l5450
29.7%
d2873
 
15.7%
b667
 
3.6%
w199
 
1.1%
r18
 
0.1%
h8
 
< 0.1%
_6
 
< 0.1%
g6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)18335
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m9108
49.7%
l5450
29.7%
d2873
 
15.7%
b667
 
3.6%
w199
 
1.1%
r18
 
0.1%
h8
 
< 0.1%
_6
 
< 0.1%
g6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)18335
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m9108
49.7%
l5450
29.7%
d2873
 
15.7%
b667
 
3.6%
w199
 
1.1%
r18
 
0.1%
h8
 
< 0.1%
_6
 
< 0.1%
g6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)18335
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m9108
49.7%
l5450
29.7%
d2873
 
15.7%
b667
 
3.6%
w199
 
1.1%
r18
 
0.1%
h8
 
< 0.1%
_6
 
< 0.1%
g6
 
< 0.1%

nst
Real number (ℝ)

High correlation  Missing 

Distinct172
Distinct (%)2.0%
Missing308
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean26.75855
Minimum0
Maximum324
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size71.3 KiB
2025-12-13T19:21:02.240340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q111
median20
Q334
95-th percentile70
Maximum324
Range324
Interquartile range (IQR)23

Descriptive statistics

Standard deviation23.822177
Coefficient of variation (CV)0.89026412
Kurtosis19.132259
Mean26.75855
Median Absolute Deviation (MAD)10
Skewness3.1774396
Sum235502
Variance567.49613
MonotonicityNot monotonic
2025-12-13T19:21:02.508404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11346
 
3.8%
10335
 
3.7%
9326
 
3.6%
8326
 
3.6%
5325
 
3.6%
13297
 
3.3%
12294
 
3.2%
15286
 
3.1%
14277
 
3.0%
7269
 
3.0%
Other values (162)5720
62.8%
(Missing)308
 
3.4%
ValueCountFrequency (%)
04
 
< 0.1%
313
 
0.1%
4135
 
1.5%
5325
3.6%
6164
1.8%
7269
3.0%
8326
3.6%
9326
3.6%
10335
3.7%
11346
3.8%
ValueCountFrequency (%)
3241
< 0.1%
3111
< 0.1%
3101
< 0.1%
2731
< 0.1%
2631
< 0.1%
2551
< 0.1%
2491
< 0.1%
2441
< 0.1%
2251
< 0.1%
2151
< 0.1%

gap
Real number (ℝ)

High correlation  Missing 

Distinct1514
Distinct (%)17.2%
Missing310
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean102.59984
Minimum12
Maximum347
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size71.3 KiB
2025-12-13T19:21:02.769640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile36
Q160
median85
Q3127
95-th percentile234.38735
Maximum347
Range335
Interquartile range (IQR)67

Descriptive statistics

Standard deviation59.997069
Coefficient of variation (CV)0.58476766
Kurtosis1.4777533
Mean102.59984
Median Absolute Deviation (MAD)30
Skewness1.3192013
Sum902776
Variance3599.6483
MonotonicityNot monotonic
2025-12-13T19:21:03.037801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56101
 
1.1%
6097
 
1.1%
7192
 
1.0%
6589
 
1.0%
5789
 
1.0%
5888
 
1.0%
6387
 
1.0%
6887
 
1.0%
8485
 
0.9%
5585
 
0.9%
Other values (1504)7899
86.7%
(Missing)310
 
3.4%
ValueCountFrequency (%)
121
 
< 0.1%
13.682067871
 
< 0.1%
142
 
< 0.1%
151
 
< 0.1%
167
0.1%
1712
0.1%
17.361953741
 
< 0.1%
187
0.1%
18.083190921
 
< 0.1%
18.619979861
 
< 0.1%
ValueCountFrequency (%)
3472
< 0.1%
3441
< 0.1%
3411
< 0.1%
3351
< 0.1%
3341
< 0.1%
333.43478391
< 0.1%
331.75604251
< 0.1%
3311
< 0.1%
328.49251561
< 0.1%
328.39694211
< 0.1%

dmin
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct6532
Distinct (%)74.2%
Missing310
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean0.42228892
Minimum0
Maximum42.586
Zeros461
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size71.3 KiB
2025-12-13T19:21:03.306842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01585
median0.06511
Q30.25462501
95-th percentile1.8961
Maximum42.586
Range42.586
Interquartile range (IQR)0.23877501

Descriptive statistics

Standard deviation1.6185278
Coefficient of variation (CV)3.8327498
Kurtosis179.70437
Mean0.42228892
Median Absolute Deviation (MAD)0.05649
Skewness11.217183
Sum3715.7202
Variance2.6196321
MonotonicityNot monotonic
2025-12-13T19:21:03.571002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0461
 
5.1%
0.1266
 
2.9%
0.5122
 
1.3%
0.274
 
0.8%
0.430
 
0.3%
0.317
 
0.2%
0.69
 
0.1%
0.77
 
0.1%
0.14126
 
0.1%
0.4596
 
0.1%
Other values (6522)7801
85.6%
(Missing)310
 
3.4%
ValueCountFrequency (%)
0461
5.1%
0.00038741
 
< 0.1%
0.00043771
 
< 0.1%
0.00044361
 
< 0.1%
0.00060831
 
< 0.1%
0.00065371
 
< 0.1%
0.00071161
 
< 0.1%
0.0007171
 
< 0.1%
0.00073021
 
< 0.1%
0.00077881
 
< 0.1%
ValueCountFrequency (%)
42.5861
< 0.1%
32.6331
< 0.1%
31.4331
< 0.1%
30.721
< 0.1%
30.6581
< 0.1%
29.4051
< 0.1%
27.2371
< 0.1%
26.7381
< 0.1%
24.7931
< 0.1%
24.1891
< 0.1%

rms
Real number (ℝ)

High correlation 

Distinct1475
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31424635
Minimum0
Maximum5.1789552
Zeros16
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size71.3 KiB
2025-12-13T19:21:03.866330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.08
median0.17
Q30.49297636
95-th percentile1
Maximum5.1789552
Range5.1789552
Interquartile range (IQR)0.41297636

Descriptive statistics

Standard deviation0.34736779
Coefficient of variation (CV)1.1053996
Kurtosis10.575825
Mean0.31424635
Median Absolute Deviation (MAD)0.12
Skewness2.1820667
Sum2862.47
Variance0.12066438
MonotonicityNot monotonic
2025-12-13T19:21:04.116428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2563
 
6.2%
0.02558
 
6.1%
0.1467
 
5.1%
0.01441
 
4.8%
0.03296
 
3.2%
0.14228
 
2.5%
0.16226
 
2.5%
0.07224
 
2.5%
0.13221
 
2.4%
0.17216
 
2.4%
Other values (1465)5669
62.2%
ValueCountFrequency (%)
016
 
0.2%
0.01441
4.8%
0.02558
6.1%
0.022196908551
 
< 0.1%
0.03296
3.2%
0.04192
 
2.1%
0.05158
 
1.7%
0.05000000071
 
< 0.1%
0.05141
 
< 0.1%
0.05671
 
< 0.1%
ValueCountFrequency (%)
5.1789551751
< 0.1%
4.2699920331
< 0.1%
4.1926867121
< 0.1%
2.9819855711
< 0.1%
2.8906165661
< 0.1%
2.4405146441
< 0.1%
2.41
< 0.1%
2.3661233161
< 0.1%
2.2439410291
< 0.1%
2.1540624431
< 0.1%

net
Categorical

High correlation 

Distinct14
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size71.3 KiB
nc
2175 
ak
1736 
ci
1157 
us
1096 
tx
744 
Other values (9)
2201 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters18218
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowak
2nd rowak
3rd rowci
4th rowci
5th rowpr

Common Values

ValueCountFrequency (%)
nc2175
23.9%
ak1736
19.1%
ci1157
12.7%
us1096
12.0%
tx744
 
8.2%
av643
 
7.1%
uu445
 
4.9%
hv361
 
4.0%
uw306
 
3.4%
ok154
 
1.7%
Other values (4)292
 
3.2%

Length

2025-12-13T19:21:04.385409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nc2175
23.9%
ak1736
19.1%
ci1157
12.7%
us1096
12.0%
tx744
 
8.2%
av643
 
7.1%
uu445
 
4.9%
hv361
 
4.0%
uw306
 
3.4%
ok154
 
1.7%
Other values (4)292
 
3.2%

Most occurring characters

ValueCountFrequency (%)
c3332
18.3%
n2389
13.1%
a2379
13.1%
u2292
12.6%
k1890
10.4%
i1157
 
6.4%
s1110
 
6.1%
v1004
 
5.5%
t744
 
4.1%
x744
 
4.1%
Other values (7)1177
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)18218
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c3332
18.3%
n2389
13.1%
a2379
13.1%
u2292
12.6%
k1890
10.4%
i1157
 
6.4%
s1110
 
6.1%
v1004
 
5.5%
t744
 
4.1%
x744
 
4.1%
Other values (7)1177
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)18218
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c3332
18.3%
n2389
13.1%
a2379
13.1%
u2292
12.6%
k1890
10.4%
i1157
 
6.4%
s1110
 
6.1%
v1004
 
5.5%
t744
 
4.1%
x744
 
4.1%
Other values (7)1177
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)18218
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c3332
18.3%
n2389
13.1%
a2379
13.1%
u2292
12.6%
k1890
10.4%
i1157
 
6.4%
s1110
 
6.1%
v1004
 
5.5%
t744
 
4.1%
x744
 
4.1%
Other values (7)1177
 
6.5%

id
Text

Unique 

Distinct9109
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size71.3 KiB
2025-12-13T19:21:04.987018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.549566
Min length10

Characters and Unicode

Total characters96096
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9109 ?
Unique (%)100.0%

Sample

1st rowak2025yhrghl
2nd rowak2025yhqyiw
3rd rowci41137119
4th rowci41137111
5th rowpr71502003
ValueCountFrequency (%)
ci411370871
 
< 0.1%
uw622039521
 
< 0.1%
ak2025yhrghl1
 
< 0.1%
ak2025yhqyiw1
 
< 0.1%
ci411371191
 
< 0.1%
ak025eh0w2rm1
 
< 0.1%
us7000regf1
 
< 0.1%
us7000reg81
 
< 0.1%
av938818011
 
< 0.1%
ci404836341
 
< 0.1%
Other values (9099)9099
99.9%
2025-12-13T19:21:05.767616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
210903
 
11.3%
09265
 
9.6%
57300
 
7.6%
76085
 
6.3%
16050
 
6.3%
65923
 
6.2%
c3942
 
4.1%
43869
 
4.0%
33514
 
3.7%
n3059
 
3.2%
Other values (26)36186
37.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)96096
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
210903
 
11.3%
09265
 
9.6%
57300
 
7.6%
76085
 
6.3%
16050
 
6.3%
65923
 
6.2%
c3942
 
4.1%
43869
 
4.0%
33514
 
3.7%
n3059
 
3.2%
Other values (26)36186
37.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)96096
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
210903
 
11.3%
09265
 
9.6%
57300
 
7.6%
76085
 
6.3%
16050
 
6.3%
65923
 
6.2%
c3942
 
4.1%
43869
 
4.0%
33514
 
3.7%
n3059
 
3.2%
Other values (26)36186
37.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)96096
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
210903
 
11.3%
09265
 
9.6%
57300
 
7.6%
76085
 
6.3%
16050
 
6.3%
65923
 
6.2%
c3942
 
4.1%
43869
 
4.0%
33514
 
3.7%
n3059
 
3.2%
Other values (26)36186
37.7%

updated
Date

Unique 

Distinct9109
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size71.3 KiB
Minimum2025-11-11 03:17:29.664000+00:00
Maximum2025-12-11 02:45:52.387000+00:00
Invalid dates0
Invalid dates (%)0.0%
2025-12-13T19:21:06.024554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:21:06.291888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

place
Text

Distinct4796
Distinct (%)52.7%
Missing0
Missing (%)0.0%
Memory size71.3 KiB
2025-12-13T19:21:07.075787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length57
Median length49
Mean length27.565924
Min length8

Characters and Unicode

Total characters251098
Distinct characters89
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3858 ?
Unique (%)42.4%

Sample

1st row70 km SSE of Cantwell, Alaska
2nd row19 km N of Fishhook, Alaska
3rd row8 km SE of Big Bear City, CA
4th row9 km SW of Idyllwild, CA
5th row1 km WSW of Guayanilla, Puerto Rico
ValueCountFrequency (%)
of8987
 
15.4%
km8947
 
15.3%
ca3303
 
5.7%
alaska2729
 
4.7%
nw1149
 
2.0%
the1043
 
1.8%
geysers1016
 
1.7%
n782
 
1.3%
w713
 
1.2%
wnw699
 
1.2%
Other values (1670)29003
49.7%
2025-12-13T19:21:08.122351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49262
19.6%
a16576
 
6.6%
o15561
 
6.2%
k13881
 
5.5%
m10215
 
4.1%
e9697
 
3.9%
f9354
 
3.7%
,9022
 
3.6%
s8844
 
3.5%
l7778
 
3.1%
Other values (79)100908
40.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)251098
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
49262
19.6%
a16576
 
6.6%
o15561
 
6.2%
k13881
 
5.5%
m10215
 
4.1%
e9697
 
3.9%
f9354
 
3.7%
,9022
 
3.6%
s8844
 
3.5%
l7778
 
3.1%
Other values (79)100908
40.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)251098
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
49262
19.6%
a16576
 
6.6%
o15561
 
6.2%
k13881
 
5.5%
m10215
 
4.1%
e9697
 
3.9%
f9354
 
3.7%
,9022
 
3.6%
s8844
 
3.5%
l7778
 
3.1%
Other values (79)100908
40.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)251098
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
49262
19.6%
a16576
 
6.6%
o15561
 
6.2%
k13881
 
5.5%
m10215
 
4.1%
e9697
 
3.9%
f9354
 
3.7%
,9022
 
3.6%
s8844
 
3.5%
l7778
 
3.1%
Other values (79)100908
40.2%

type
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.3 KiB
earthquake
8946 
quarry blast
 
76
explosion
 
73
ice quake
 
14

Length

Max length12
Median length10
Mean length10.007136
Min length9

Characters and Unicode

Total characters91155
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowearthquake
2nd rowearthquake
3rd rowearthquake
4th rowearthquake
5th rowearthquake

Common Values

ValueCountFrequency (%)
earthquake8946
98.2%
quarry blast76
 
0.8%
explosion73
 
0.8%
ice quake14
 
0.2%

Length

2025-12-13T19:21:08.340648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-13T19:21:08.495133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
earthquake8946
97.2%
quarry76
 
0.8%
blast76
 
0.8%
explosion73
 
0.8%
ice14
 
0.2%
quake14
 
0.2%

Most occurring characters

ValueCountFrequency (%)
a18058
19.8%
e17993
19.7%
r9098
10.0%
u9036
9.9%
q9036
9.9%
t9022
9.9%
k8960
9.8%
h8946
9.8%
l149
 
0.2%
s149
 
0.2%
Other values (9)708
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)91155
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a18058
19.8%
e17993
19.7%
r9098
10.0%
u9036
9.9%
q9036
9.9%
t9022
9.9%
k8960
9.8%
h8946
9.8%
l149
 
0.2%
s149
 
0.2%
Other values (9)708
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)91155
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a18058
19.8%
e17993
19.7%
r9098
10.0%
u9036
9.9%
q9036
9.9%
t9022
9.9%
k8960
9.8%
h8946
9.8%
l149
 
0.2%
s149
 
0.2%
Other values (9)708
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)91155
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a18058
19.8%
e17993
19.7%
r9098
10.0%
u9036
9.9%
q9036
9.9%
t9022
9.9%
k8960
9.8%
h8946
9.8%
l149
 
0.2%
s149
 
0.2%
Other values (9)708
 
0.8%

horizontalError
Real number (ℝ)

Missing  Zeros 

Distinct1678
Distinct (%)19.6%
Missing553
Missing (%)6.1%
Infinite0
Infinite (%)0.0%
Mean1.3082606
Minimum0
Maximum26.59
Zeros1429
Zeros (%)15.7%
Negative0
Negative (%)0.0%
Memory size71.3 KiB
2025-12-13T19:21:08.732405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.18
median0.33
Q30.73
95-th percentile8.35
Maximum26.59
Range26.59
Interquartile range (IQR)0.55

Descriptive statistics

Standard deviation2.7563756
Coefficient of variation (CV)2.106901
Kurtosis10.480106
Mean1.3082606
Median Absolute Deviation (MAD)0.21
Skewness3.1870808
Sum11193.478
Variance7.5976067
MonotonicityNot monotonic
2025-12-13T19:21:09.009822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01429
 
15.7%
0.21175
 
1.9%
0.24168
 
1.8%
0.22164
 
1.8%
0.25154
 
1.7%
0.23154
 
1.7%
0.19146
 
1.6%
0.29145
 
1.6%
0.27145
 
1.6%
0.2139
 
1.5%
Other values (1668)5737
63.0%
(Missing)553
 
6.1%
ValueCountFrequency (%)
01429
15.7%
0.082
 
< 0.1%
0.0911
 
0.1%
0.118
 
0.2%
0.1126
 
0.3%
0.1244
 
0.5%
0.1354
 
0.6%
0.1483
 
0.9%
0.15110
 
1.2%
0.16121
 
1.3%
ValueCountFrequency (%)
26.591
< 0.1%
19.951
< 0.1%
19.691
< 0.1%
19.351
< 0.1%
19.121
< 0.1%
19.051
< 0.1%
18.361
< 0.1%
18.071
< 0.1%
18.041
< 0.1%
17.881
< 0.1%

depthError
Real number (ℝ)

High correlation 

Distinct3466
Distinct (%)38.4%
Missing81
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean2.3888875
Minimum0
Maximum115.88538
Zeros88
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size71.3 KiB
2025-12-13T19:21:09.243190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.29
Q10.5
median0.86659007
Q32.14
95-th percentile8.3235
Maximum115.88538
Range115.88538
Interquartile range (IQR)1.64

Descriptive statistics

Standard deviation4.7176496
Coefficient of variation (CV)1.9748312
Kurtosis72.00844
Mean2.3888875
Median Absolute Deviation (MAD)0.49659006
Skewness6.4043681
Sum21566.877
Variance22.256218
MonotonicityNot monotonic
2025-12-13T19:21:09.480758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5124
 
1.4%
31.61122
 
1.3%
0.3118
 
1.3%
0.4112
 
1.2%
088
 
1.0%
0.3774
 
0.8%
0.671
 
0.8%
0.4669
 
0.8%
0.3466
 
0.7%
0.4165
 
0.7%
Other values (3456)8119
89.1%
(Missing)81
 
0.9%
ValueCountFrequency (%)
088
1.0%
0.081
 
< 0.1%
0.13
 
< 0.1%
0.112
 
< 0.1%
0.123
 
< 0.1%
0.133
 
< 0.1%
0.146
 
0.1%
0.154
 
< 0.1%
0.1500000061
 
< 0.1%
0.1610
 
0.1%
ValueCountFrequency (%)
115.8853761
 
< 0.1%
84.93771
 
< 0.1%
67.48671
 
< 0.1%
41.124130251
 
< 0.1%
40.710536961
 
< 0.1%
35.091
 
< 0.1%
34.81
 
< 0.1%
32.765808111
 
< 0.1%
31.61122
1.3%
28.751
 
< 0.1%

magError
Real number (ℝ)

Missing 

Distinct4467
Distinct (%)51.0%
Missing352
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean0.19745428
Minimum0
Maximum1.45
Zeros42
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size71.3 KiB
2025-12-13T19:21:09.744510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05
Q10.11
median0.17548281
Q30.24717988
95-th percentile0.43072263
Maximum1.45
Range1.45
Interquartile range (IQR)0.13717988

Descriptive statistics

Standard deviation0.12697903
Coefficient of variation (CV)0.64308063
Kurtosis9.883018
Mean0.19745428
Median Absolute Deviation (MAD)0.068345311
Skewness2.153045
Sum1729.1072
Variance0.016123673
MonotonicityNot monotonic
2025-12-13T19:21:10.051646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1477
 
5.2%
0.2462
 
5.1%
0.15115
 
1.3%
0.392
 
1.0%
0.1288
 
1.0%
0.1386
 
0.9%
0.1184
 
0.9%
0.1483
 
0.9%
0.1783
 
0.9%
0.1683
 
0.9%
Other values (4457)7104
78.0%
(Missing)352
 
3.9%
ValueCountFrequency (%)
042
0.5%
0.00056225217441
 
< 0.1%
0.00081223472991
 
< 0.1%
0.0011
 
< 0.1%
0.0021
 
< 0.1%
0.0031
 
< 0.1%
0.0070065767241
 
< 0.1%
0.0091
 
< 0.1%
0.012
 
< 0.1%
0.010916241931
 
< 0.1%
ValueCountFrequency (%)
1.451
< 0.1%
1.4028191091
< 0.1%
1.41
< 0.1%
1.381
< 0.1%
1.371
< 0.1%
1.291
< 0.1%
1.261
< 0.1%
1.171
< 0.1%
1.0587924431
< 0.1%
1.0509775351
< 0.1%

magNst
Real number (ℝ)

High correlation  Missing 

Distinct180
Distinct (%)2.0%
Missing309
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean18.386818
Minimum0
Maximum754
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size71.3 KiB
2025-12-13T19:21:10.384886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q16
median11
Q320
95-th percentile57
Maximum754
Range754
Interquartile range (IQR)14

Descriptive statistics

Standard deviation27.865056
Coefficient of variation (CV)1.5154909
Kurtosis142.68873
Mean18.386818
Median Absolute Deviation (MAD)6
Skewness8.8966846
Sum161804
Variance776.46133
MonotonicityNot monotonic
2025-12-13T19:21:10.608876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4791
 
8.7%
5637
 
7.0%
7527
 
5.8%
6505
 
5.5%
8469
 
5.1%
10436
 
4.8%
9379
 
4.2%
12329
 
3.6%
13323
 
3.5%
11317
 
3.5%
Other values (170)4087
44.9%
(Missing)309
 
3.4%
ValueCountFrequency (%)
08
 
0.1%
137
 
0.4%
2106
 
1.2%
3208
 
2.3%
4791
8.7%
5637
7.0%
6505
5.5%
7527
5.8%
8469
5.1%
9379
4.2%
ValueCountFrequency (%)
7541
< 0.1%
6691
< 0.1%
5021
< 0.1%
4741
< 0.1%
4501
< 0.1%
3751
< 0.1%
3511
< 0.1%
3421
< 0.1%
3392
< 0.1%
3211
< 0.1%

status
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.3 KiB
reviewed
6473 
automatic
2636 

Length

Max length9
Median length8
Mean length8.2893841
Min length8

Characters and Unicode

Total characters75508
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowautomatic
2nd rowautomatic
3rd rowautomatic
4th rowautomatic
5th rowreviewed

Common Values

ValueCountFrequency (%)
reviewed6473
71.1%
automatic2636
28.9%

Length

2025-12-13T19:21:10.853178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-13T19:21:10.989528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
reviewed6473
71.1%
automatic2636
28.9%

Most occurring characters

ValueCountFrequency (%)
e19419
25.7%
i9109
12.1%
r6473
 
8.6%
v6473
 
8.6%
w6473
 
8.6%
d6473
 
8.6%
a5272
 
7.0%
t5272
 
7.0%
u2636
 
3.5%
o2636
 
3.5%
Other values (2)5272
 
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)75508
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e19419
25.7%
i9109
12.1%
r6473
 
8.6%
v6473
 
8.6%
w6473
 
8.6%
d6473
 
8.6%
a5272
 
7.0%
t5272
 
7.0%
u2636
 
3.5%
o2636
 
3.5%
Other values (2)5272
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)75508
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e19419
25.7%
i9109
12.1%
r6473
 
8.6%
v6473
 
8.6%
w6473
 
8.6%
d6473
 
8.6%
a5272
 
7.0%
t5272
 
7.0%
u2636
 
3.5%
o2636
 
3.5%
Other values (2)5272
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)75508
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e19419
25.7%
i9109
12.1%
r6473
 
8.6%
v6473
 
8.6%
w6473
 
8.6%
d6473
 
8.6%
a5272
 
7.0%
t5272
 
7.0%
u2636
 
3.5%
o2636
 
3.5%
Other values (2)5272
 
7.0%

locationSource
Categorical

High correlation 

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size71.3 KiB
nc
2175 
ak
1736 
ci
1157 
us
1094 
tx
744 
Other values (10)
2203 

Length

Max length4
Median length2
Mean length2.0004391
Min length2

Characters and Unicode

Total characters18222
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowak
2nd rowak
3rd rowci
4th rowci
5th rowpr

Common Values

ValueCountFrequency (%)
nc2175
23.9%
ak1736
19.1%
ci1157
12.7%
us1094
12.0%
tx744
 
8.2%
av643
 
7.1%
uu445
 
4.9%
hv361
 
4.0%
uw306
 
3.4%
ok154
 
1.7%
Other values (5)294
 
3.2%

Length

2025-12-13T19:21:11.143392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nc2175
23.9%
ak1736
19.1%
ci1157
12.7%
us1094
12.0%
tx744
 
8.2%
av643
 
7.1%
uu445
 
4.9%
hv361
 
4.0%
uw306
 
3.4%
ok154
 
1.7%
Other values (5)294
 
3.2%

Most occurring characters

ValueCountFrequency (%)
c3334
18.3%
n2389
13.1%
a2379
13.1%
u2290
12.6%
k1890
10.4%
i1157
 
6.3%
s1110
 
6.1%
v1004
 
5.5%
t744
 
4.1%
x744
 
4.1%
Other values (7)1181
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)18222
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c3334
18.3%
n2389
13.1%
a2379
13.1%
u2290
12.6%
k1890
10.4%
i1157
 
6.3%
s1110
 
6.1%
v1004
 
5.5%
t744
 
4.1%
x744
 
4.1%
Other values (7)1181
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)18222
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c3334
18.3%
n2389
13.1%
a2379
13.1%
u2290
12.6%
k1890
10.4%
i1157
 
6.3%
s1110
 
6.1%
v1004
 
5.5%
t744
 
4.1%
x744
 
4.1%
Other values (7)1181
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)18222
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c3334
18.3%
n2389
13.1%
a2379
13.1%
u2290
12.6%
k1890
10.4%
i1157
 
6.3%
s1110
 
6.1%
v1004
 
5.5%
t744
 
4.1%
x744
 
4.1%
Other values (7)1181
 
6.5%

magSource
Categorical

High correlation 

Distinct14
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size71.3 KiB
nc
2175 
ak
1735 
ci
1157 
us
1097 
tx
744 
Other values (9)
2201 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters18218
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowak
2nd rowak
3rd rowci
4th rowci
5th rowpr

Common Values

ValueCountFrequency (%)
nc2175
23.9%
ak1735
19.0%
ci1157
12.7%
us1097
12.0%
tx744
 
8.2%
av643
 
7.1%
uu445
 
4.9%
hv361
 
4.0%
uw306
 
3.4%
ok154
 
1.7%
Other values (4)292
 
3.2%

Length

2025-12-13T19:21:11.339299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nc2175
23.9%
ak1735
19.0%
ci1157
12.7%
us1097
12.0%
tx744
 
8.2%
av643
 
7.1%
uu445
 
4.9%
hv361
 
4.0%
uw306
 
3.4%
ok154
 
1.7%
Other values (4)292
 
3.2%

Most occurring characters

ValueCountFrequency (%)
c3332
18.3%
n2389
13.1%
a2378
13.1%
u2293
12.6%
k1889
10.4%
i1157
 
6.4%
s1111
 
6.1%
v1004
 
5.5%
t744
 
4.1%
x744
 
4.1%
Other values (7)1177
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)18218
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c3332
18.3%
n2389
13.1%
a2378
13.1%
u2293
12.6%
k1889
10.4%
i1157
 
6.4%
s1111
 
6.1%
v1004
 
5.5%
t744
 
4.1%
x744
 
4.1%
Other values (7)1177
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)18218
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c3332
18.3%
n2389
13.1%
a2378
13.1%
u2293
12.6%
k1889
10.4%
i1157
 
6.4%
s1111
 
6.1%
v1004
 
5.5%
t744
 
4.1%
x744
 
4.1%
Other values (7)1177
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)18218
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c3332
18.3%
n2389
13.1%
a2378
13.1%
u2293
12.6%
k1889
10.4%
i1157
 
6.4%
s1111
 
6.1%
v1004
 
5.5%
t744
 
4.1%
x744
 
4.1%
Other values (7)1177
 
6.5%

Interactions

2025-12-13T19:20:55.025193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:27.135906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:29.594984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:31.981676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:34.339874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:36.937004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:39.450601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:42.230929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:44.661017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:47.332656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:49.854067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:52.308326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:55.268463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:27.365577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:29.785720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:32.172692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:34.543588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:37.154625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:39.695216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:42.450668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:44.934205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:47.556247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:50.049674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:52.519435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:55.476769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:27.555580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:29.963635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:32.360057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:34.736051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:37.350874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:39.993050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:42.659550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:45.155171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:47.783010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:50.280958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:52.747462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:55.704192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:27.737051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:30.167789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:32.556135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:34.923232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:37.544963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:40.181722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:42.856770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:45.327869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:47.999996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:50.531817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:52.962491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:55.892199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:27.935707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:30.405615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:32.733366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:35.077881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:37.758417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:40.369961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:43.042423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:45.485182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:48.191067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:50.718904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:53.155337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:56.082578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:28.149239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:30.596112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:32.934039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:35.255378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:37.942077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:40.639842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:43.231128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:45.719242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:48.423660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:50.908089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:53.364344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:56.313234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:28.365831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:30.786797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:33.124866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:35.748255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:38.135093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:40.875853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:43.452243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:45.963987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:48.657578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:51.107342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:53.570271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:56.526176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:28.538368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:30.980766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:33.316063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:35.916637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:38.339296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:41.050717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:43.641114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:46.152583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:48.833661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:51.303496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:53.823913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:56.743743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:28.759677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:31.188091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:33.498364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:36.094625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:38.578292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:41.232117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:43.822015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:46.351593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:49.047717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:51.475400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:54.111208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:56.966000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:28.949457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:31.365142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:33.687513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:36.321891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:38.781058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:41.457488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:43.997102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:46.707263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:49.246175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:51.678393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:54.343171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:57.216270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:29.149481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:31.548985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:33.912471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:36.517650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:38.991136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:41.696171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:44.220309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:46.897750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:49.434779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:51.908221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:54.569435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:57.436438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:29.353462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:31.775562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:34.153759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:36.709898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:39.222325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:41.966478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:44.459687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:47.118985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:49.643125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:52.117434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T19:20:54.806453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-13T19:21:11.519358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
depthdepthErrordmingaphorizontalErrorlatitudelocationSourcelongitudemagmagErrormagNstmagSourcemagTypenetnstrmsstatustype
depth1.0000.3200.5330.0410.075-0.0800.1670.0190.491-0.0450.1260.1670.1640.1670.4180.5030.1270.000
depthError0.3201.0000.6180.3380.2410.2290.157-0.1180.387-0.018-0.1960.1570.0530.157-0.1070.4980.0740.416
dmin0.5330.6181.0000.1560.0590.1470.129-0.0200.645-0.0770.0820.1290.1830.1290.2930.7430.0810.000
gap0.0410.3380.1561.0000.4130.1170.222-0.208-0.027-0.002-0.3710.2220.0930.222-0.5140.0060.0720.051
horizontalError0.0750.2410.0590.4131.000-0.3950.3130.3260.102-0.4560.1270.3120.3470.312-0.1710.0060.2020.000
latitude-0.0800.2290.1470.117-0.3951.0000.528-0.613-0.0390.328-0.3510.5280.3300.528-0.2870.2300.2620.139
locationSource0.1670.1570.1290.2220.3130.5281.0000.5570.4190.2420.1111.0000.4721.0000.1640.3140.6150.320
longitude0.019-0.118-0.020-0.2080.326-0.6130.5571.0000.064-0.2960.2490.5510.3550.5510.215-0.0650.3230.072
mag0.4910.3870.645-0.0270.102-0.0390.4190.0641.000-0.1670.3740.4180.4480.4170.5520.6750.3070.061
magError-0.045-0.018-0.077-0.002-0.4560.3280.242-0.296-0.1671.000-0.3190.2430.1410.243-0.1250.0160.1730.079
magNst0.126-0.1960.082-0.3710.127-0.3510.1110.2490.374-0.3191.0000.1120.1060.1110.6730.1150.0950.000
magSource0.1670.1570.1290.2220.3120.5281.0000.5510.4180.2430.1121.0000.4691.0000.1640.3140.6150.320
magType0.1640.0530.1830.0930.3470.3300.4720.3550.4480.1410.1060.4691.0000.4680.2120.2080.3630.050
net0.1670.1570.1290.2220.3120.5281.0000.5510.4170.2430.1111.0000.4681.0000.1640.3140.6150.320
nst0.418-0.1070.293-0.514-0.171-0.2870.1640.2150.552-0.1250.6730.1640.2120.1641.0000.4250.1330.024
rms0.5030.4980.7430.0060.0060.2300.314-0.0650.6750.0160.1150.3140.2080.3140.4251.0000.0750.114
status0.1270.0740.0810.0720.2020.2620.6150.3230.3070.1730.0950.6150.3630.6150.1330.0751.0000.084
type0.0000.4160.0000.0510.0000.1390.3200.0720.0610.0790.0000.3200.0500.3200.0240.1140.0841.000

Missing values

2025-12-13T19:20:57.823219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-13T19:20:58.220245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-12-13T19:20:58.638609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

timelatitudelongitudedepthmagmagTypenstgapdminrmsnetidupdatedplacetypehorizontalErrordepthErrormagErrormagNststatuslocationSourcemagSource
02025-12-11T02:44:15.295Z62.841000-148.27100063.7002.30ml45.040.00.100000.80akak2025yhrghl2025-12-11T02:45:52.387Z70 km SSE of Cantwell, Alaskaearthquake0.002.95810.4000004.0automaticakak
12025-12-11T02:35:00.746Z61.918000-149.2670002.6002.00ml23.055.00.200000.90akak2025yhqyiw2025-12-11T02:37:16.578Z19 km N of Fishhook, Alaskaearthquake0.002.47200.4000006.0automaticakak
22025-12-11T02:21:24.210Z34.200167-116.7900001.4600.80ml29.049.00.124800.12cici411371192025-12-11T02:25:01.452Z8 km SE of Big Bear City, CAearthquake0.130.37000.16460110.0automaticcici
32025-12-11T02:11:50.960Z33.677167-116.77850017.8000.98ml52.026.00.066630.15cici411371112025-12-11T02:22:28.550Z9 km SW of Idyllwild, CAearthquake0.160.29000.19469727.0automaticcici
42025-12-11T01:58:23.900Z18.014667-66.80733315.8902.35md11.0154.00.048360.16prpr715020032025-12-11T02:19:14.620Z1 km WSW of Guayanilla, Puerto Ricoearthquake0.440.75000.1348385.0reviewedprpr
52025-12-11T01:51:37.200Z35.705000-117.5178339.0702.36ml45.054.00.086350.15cici411370872025-12-11T02:07:14.335Z12 km SW of Searles Valley, CAearthquake0.140.44000.18200625.0reviewedcici
62025-12-11T01:38:11.516Z60.548800-139.7498005.0003.00ml32.072.00.519000.80usus6000ru1t2025-12-11T02:10:44.040Z111 km N of Yakutat, Alaskaearthquake3.281.98300.04000083.0reviewedusus
72025-12-11T01:29:19.022Z60.407600-139.5118004.4312.80ml25.0110.00.484000.78usus6000ru1q2025-12-11T02:28:12.040Z96 km N of Yakutat, Alaskaearthquake2.197.68600.05100050.0reviewedusus
82025-12-11T01:28:27.170Z33.385667-116.75050012.5000.25ml23.043.00.099150.14cici411370792025-12-11T01:32:00.985Z11 km ENE of Palomar Observatory, CAearthquake0.200.67000.1684005.0automaticcici
92025-12-11T01:06:11.040Z34.160167-117.78716713.8701.72ml14.082.00.069090.12cici411370712025-12-11T01:09:52.975Z6 km NNE of San Dimas, CAearthquake0.340.54000.32291522.0automaticcici
timelatitudelongitudedepthmagmagTypenstgapdminrmsnetidupdatedplacetypehorizontalErrordepthErrormagErrormagNststatuslocationSourcemagSource
90992025-11-11T03:53:39.388Z31.707000-104.1750007.38161.90ml43.062.00.1000000.20txtx2025wertnc2025-11-11T08:09:08.712Z54 km W of Mentone, Texasearthquake0.4478080.6136150.10000023.0reviewedtxtx
91002025-11-11T03:46:31.723Z61.069400-152.466900126.30001.60mlNaNNaNNaN0.24akak025eh0cjr82025-11-11T03:48:30.859Z71 km W of Tyonek, AlaskaearthquakeNaN2.100000NaNNaNautomaticakak
91012025-11-11T03:44:28.924Z59.460100-151.38140052.70001.90mlNaNNaNNaN0.74akak025eh0c4672025-11-11T03:48:20.988Z15 km E of Seldovia Village, AlaskaearthquakeNaN0.500000NaNNaNautomaticakak
91022025-11-11T03:35:09.463Z57.217800-156.49440014.20002.50mlNaNNaNNaN0.84akak025eh0a55u2025-12-07T23:35:13.040Z63 km ESE of Ugashik, AlaskaearthquakeNaN1.100000NaNNaNautomaticakak
91032025-11-11T03:28:36.731Z13.755300-58.46720010.00005.00mww124.059.02.3930000.62usus6000rmpy2025-12-07T23:33:48.678Z128 km NE of Crane, Barbadosearthquake8.1600001.8710000.09300011.0reviewedusus
91042025-11-11T03:24:39.120Z38.821335-122.8470001.67001.07md13.080.00.0095180.02ncnc752617512025-11-11T04:52:20.464Z9 km WNW of The Geysers, CAearthquake0.2700000.4100000.15000014.0automaticncnc
91052025-11-11T03:18:55.840Z37.763667-121.9328337.26000.62md14.086.00.0228600.04ncnc752617462025-11-21T11:10:59.382Z4 km ESE of San Ramon, CAearthquake0.2100000.4000000.27400011.0reviewedncnc
91062025-11-11T03:15:44.139Z60.646800-147.66140017.90001.90mlNaNNaNNaN0.84akak025eh05xil2025-11-11T03:17:29.664Z57 km ESE of Whittier, AlaskaearthquakeNaN0.100000NaNNaNautomaticakak
91072025-11-11T02:55:41.730Z37.599667-121.5873337.79001.14md23.070.00.0153700.08ncnc752617362025-11-21T11:42:19.050Z18 km ESE of Livermore, CAearthquake0.1900000.3100000.13400013.0reviewedncnc
91082025-11-11T02:55:00.360Z46.190833-122.1966678.43000.15ml12.098.00.0121600.13uwuw622039522025-11-11T19:00:23.800Z36 km NNE of Amboy, Washingtonearthquake0.4400000.6400000.1493825.0revieweduwuw